Network Backbones: We use two neural architectures in our experiments: Conv-4 and ResNet-12. Conv-4 consists of four convolutional layers with kernel size \(3\times 3\)  and output size 64, followed by BatchNorm, ReLU, and \(2\times 2\)  max-pooling. ResNet-12 consists of four residual blocks, each with three convolutional layers, with leaky ReLU (0.1) and \(2\times 2\)  max-pooling on the main stem. We use drop-block as in the original implementation [1]}, [2]}, [3]}https://github.com/WangYueFt/rfshttps://github.com/Sha-Lab/FEAThttps://github.com/kjunelee/MetaOptNet.
Output sizes for each residual block are: \(64, 160, 320, 640\) .
